Automation and manufacturing industries have evolved into members of a global economy in which formation of strategic partnerships amongst companies and associated components has become increasingly more important. Traditional, centralized, sequential information-processing methodologies commonly no longer meet the demands of rapidly changing manufacturing environments. The ability of an organization to quickly respond to changes and maintain productivity is increasingly limited by its information processing capabilities. Evolving distributed organizations promise high decentralization of operations. In such organizations, there are neither predefined hierarchies nor rigid structures. Instead, the organization emerges as a result of dynamic interactions of its intelligent components. Supporting infrastructures should be capable of providing timely communication and accessible resources based on the needs of an emergent organization. Constraints and information should be efficiently propagated throughout the system using mechanisms that do not fracture the enterprise. A collaborative intelligent architecture is required to enable real-time evaluation of tasks, wherein each member of the collaboration environment must adapt to the needs and regulations of its partners.
The automation and manufacturing industries commonly utilize industrial controllers to control systems therein. In general, industrial controllers are special purpose processing devices used for controlling (e.g., automated and semi-automated) industrial processes, machines, manufacturing equipment, plants, and the like. A typical controller executes a control program or routine in order to measure one or more process variables or inputs representative of the status of a controlled process and/or effectuate outputs associated with control of the process. Such inputs and outputs can be digital and/or analog, assuming a continuous range of values. A typical control routine can be created in a controller configuration environment that has various tools and interfaces whereby a developer can construct and implement a control strategy using industrial and conventional programming languages or graphical representations of control functionality. Such control routine can be downloaded from the configuration system into one or more controllers for implementation of the control strategy in controlling a process or machine.
Measured inputs received from a controlled process and outputs transmitted to the process can pass through one or more input/output (I/O) modules in a control system. Such modules can serve in the capacity of an electrical interface between the controller and the controlled process and can be located local or remote from the controller. Inputs and outputs can be recorded in an I/O memory. The input values can be asynchronously or synchronously read from the controlled process by one or more input modules and output values can be written directly to memory by a processor for subsequent communication to the process by specialized communications circuitry. An output module can interface directly with a controlled process by providing an output from memory to an actuator such as a motor, drive, valve, solenoid, and the like.
In distributed control systems, controller hardware configuration can be facilitated by separating the industrial controller into a number of control elements, each of which performs a different function. Particular control modules needed for the control task can then be connected together on a common backplane within a rack and/or through a network or other communications medium. The control modules can include processors, power supplies, network communication modules, and I/O modules exchanging input and output signals directly with the controlled process. Data can be exchanged between modules using a backplane communications bus, which can be serial or parallel, or via a network. In addition to performing I/O operations based solely on network communications, smart modules exist which can execute autonomous logical or other control programs or routines. Various control modules of a distributed industrial control system can be spatially distributed along a common communication link in several locations. Certain I/O modules can thus be located proximate a portion of the controlled equipment, and away from the controller. Data can be communicated with these remote modules over a common communication link, or network, wherein all modules on the network communicate via standard communication protocols.
Intelligent agent technology can be utilized in connection with industrial controllers to render highly decentralized, distributed, robust and flexible control of systems. In such systems, the intelligent agents can execute within industrial controllers along with control routines. Such systems can be considered a community of integrated (e.g., via plug-and-play) autonomous and efficiently cooperating units, or agents. The agents are autonomous in the sense that they independently make local decisions. Although autonomous, such agents also cooperate with each other to achieve global-level goals. Unlike classical control systems that have a centralized control system, the autonomous agent-based technique does not utilize a central control element to affect the behavior of any agent. For example, there is no control element that would be aware of all the particular states or patterns of behavior of the overall system. The overall behavior of such a system emerges from asynchronously executed decision-making processes of particular agents and from dynamically changing patterns of inter-agent interactions.
Conventional techniques utilized to validate control strategies for such agent-based systems typically utilize experimental testing with a physical target system prior to commissioning a solution for deployment. Such testing can be relatively expensive and time intensive and, for certain applications, not realistic. In addition, modifying such systems (agents, control routines and/or the physical system) and/or injecting error conditions may require new software, builds and reloads and re-design of the physical target system, leading to further costs and time consumption.